{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we read in packages that we will need for our analysis: <br>\n",
    "Numpy/Scipy for statistical computing. https://numpy.org/doc/ <br>\n",
    "Pandas for reading in our data (csv file in the case of your assignment). https://pandas.pydata.org/docs/reference/index.html#api <br>\n",
    "Matplotlib/seaborn for plotting. https://matplotlib.org/contents.html <br>\n",
    "sklearn for most of our algorithmic analysis. https://scikit-learn.org/stable/ <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packages\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Exploratory Data Analysis and Feature Engineering\n",
    "---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>151.242035</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>151.290979</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>151.072890</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>f</td>\n",
       "      <td>260</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>151.195131</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>within a day</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>151.211849</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  price host_response_time  host_response_rate  host_acceptance_rate  \\\n",
       "0  2000    199     within an hour                1.00                  1.00   \n",
       "1  2001     95                NaN                 NaN                  0.83   \n",
       "2  2002    156     within an hour                0.91                  0.98   \n",
       "3  2003    100     within an hour                0.99                  0.97   \n",
       "4  2004    100       within a day                1.00                   NaN   \n",
       "\n",
       "  host_is_superhost  host_listings_count host_identity_verified   latitude  \\\n",
       "0                 t                    5                      f -33.918732   \n",
       "1                 t                    1                      f -33.698425   \n",
       "2                 f                    8                      f -33.847388   \n",
       "3                 f                  260                      f -33.870261   \n",
       "4                 f                    1                      f -33.908168   \n",
       "\n",
       "    longitude  ... review_scores_cleanliness review_scores_checkin  \\\n",
       "0  151.242035  ...                      10.0                   9.0   \n",
       "1  151.290979  ...                      10.0                  10.0   \n",
       "2  151.072890  ...                       9.0                  10.0   \n",
       "3  151.195131  ...                      10.0                  10.0   \n",
       "4  151.211849  ...                       NaN                   NaN   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                         10.0                    10.0                 10.0   \n",
       "1                         10.0                    10.0                 10.0   \n",
       "2                         10.0                    10.0                  9.0   \n",
       "3                         10.0                    10.0                 10.0   \n",
       "4                          NaN                     NaN                  NaN   \n",
       "\n",
       "   instant_bookable          cancellation_policy  \\\n",
       "0                 t                     flexible   \n",
       "1                 t                     moderate   \n",
       "2                 f  strict_14_with_grace_period   \n",
       "3                 t  strict_14_with_grace_period   \n",
       "4                 f                     moderate   \n",
       "\n",
       "   require_guest_profile_picture  require_guest_phone_verification  \\\n",
       "0                              f                                 f   \n",
       "1                              f                                 f   \n",
       "2                              f                                 f   \n",
       "3                              f                                 f   \n",
       "4                              f                                 f   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               0.23  \n",
       "1               0.83  \n",
       "2               6.90  \n",
       "3               1.32  \n",
       "4               0.07  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in csv files and plot top of training data\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data dimensions:  (2000, 36)\n",
      "Testing data dimensions:  (2000, 35)\n"
     ]
    }
   ],
   "source": [
    "# Check dimensions of the data\n",
    "dim_train = np.shape(train)\n",
    "print(\"Training data dimensions: \", dim_train)\n",
    "\n",
    "# Testing data has one less dimension since there is no response variable\n",
    "dim_test = np.shape(test)\n",
    "print(\"Testing data dimensions: \", dim_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the column 'Id' for further work and submition\n",
    "test_id = test['Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *1.2.1. Examine Missingness*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_response_time                  935\n",
       "host_response_rate                  935\n",
       "host_acceptance_rate                713\n",
       "security_deposit                    707\n",
       "cleaning_fee                        523\n",
       "review_scores_accuracy              494\n",
       "review_scores_value                 493\n",
       "review_scores_location              493\n",
       "review_scores_checkin               493\n",
       "review_scores_communication         491\n",
       "review_scores_cleanliness           491\n",
       "review_scores_rating                491\n",
       "reviews_per_month                   430\n",
       "beds                                 12\n",
       "bedrooms                              2\n",
       "maximum_nights                        0\n",
       "longitude                             0\n",
       "price                                 0\n",
       "require_guest_profile_picture         0\n",
       "cancellation_policy                   0\n",
       "instant_bookable                      0\n",
       "host_is_superhost                     0\n",
       "host_listings_count                   0\n",
       "host_identity_verified                0\n",
       "latitude                              0\n",
       "room_type                             0\n",
       "property_type                         0\n",
       "minimum_nights                        0\n",
       "accommodates                          0\n",
       "bathrooms                             0\n",
       "bed_type                              0\n",
       "require_guest_phone_verification      0\n",
       "number_of_reviews                     0\n",
       "guests_included                       0\n",
       "extra_people                          0\n",
       "Id                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missingness of the training data\n",
    "train.isna().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_response_time                  976\n",
       "host_response_rate                  976\n",
       "host_acceptance_rate                718\n",
       "security_deposit                    670\n",
       "cleaning_fee                        501\n",
       "review_scores_value                 487\n",
       "review_scores_location              486\n",
       "review_scores_accuracy              486\n",
       "review_scores_communication         485\n",
       "review_scores_checkin               485\n",
       "review_scores_cleanliness           485\n",
       "review_scores_rating                484\n",
       "reviews_per_month                   414\n",
       "bedrooms                              4\n",
       "beds                                  3\n",
       "maximum_nights                        0\n",
       "property_type                         0\n",
       "require_guest_profile_picture         0\n",
       "cancellation_policy                   0\n",
       "instant_bookable                      0\n",
       "host_is_superhost                     0\n",
       "host_listings_count                   0\n",
       "host_identity_verified                0\n",
       "latitude                              0\n",
       "longitude                             0\n",
       "room_type                             0\n",
       "minimum_nights                        0\n",
       "accommodates                          0\n",
       "bathrooms                             0\n",
       "bed_type                              0\n",
       "number_of_reviews                     0\n",
       "require_guest_phone_verification      0\n",
       "guests_included                       0\n",
       "extra_people                          0\n",
       "Id                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missingness of the testing data\n",
    "test.isna().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['host_response_time', 'host_response_rate', 'host_acceptance_rate',\n",
       "       'bedrooms', 'beds', 'security_deposit', 'cleaning_fee',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort out the column with missingness from both train and test set \n",
    "train_missing = train.columns[train.isna().sum()>0]\n",
    "test_missing = test.columns[test.isna().sum()>0]\n",
    "train_missing.join(test_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *1.2.2. Handling Missingness*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       within an hour\n",
      "1                  NaN\n",
      "2       within an hour\n",
      "3       within an hour\n",
      "4         within a day\n",
      "             ...      \n",
      "1995    within an hour\n",
      "1996    within an hour\n",
      "1997    within an hour\n",
      "1998      within a day\n",
      "1999    within an hour\n",
      "Name: host_response_time, Length: 2000, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Overview the 1st variable with missingness\n",
    "print(train['host_response_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_response_time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a few days or more</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within a day</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within a few hours</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within an hour</th>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0               count\n",
       "host_response_time       \n",
       "a few days or more     34\n",
       "within a day          123\n",
       "within a few hours    166\n",
       "within an hour        742"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check different sub-classes from the variable\n",
    "pd.crosstab(index=train[\"host_response_time\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_response_time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a few days or more</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within 12 hours</th>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within a day</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within a few hours</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within an hour</th>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0               count\n",
       "host_response_time       \n",
       "a few days or more     34\n",
       "within 12 hours       935\n",
       "within a day          123\n",
       "within a few hours    166\n",
       "within an hour        742"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the NaN with a new category\n",
    "train['host_response_time'] = train['host_response_time'].fillna('within 12 hours')\n",
    "test['host_response_time'] = test['host_response_time'].fillna('within 12 hours')\n",
    "\n",
    "# Show the new sub_classes\n",
    "pd.crosstab(index=train[\"host_response_time\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1.00\n",
      "1        NaN\n",
      "2       0.91\n",
      "3       0.99\n",
      "4       1.00\n",
      "        ... \n",
      "1995    1.00\n",
      "1996    1.00\n",
      "1997    1.00\n",
      "1998    0.80\n",
      "1999    1.00\n",
      "Name: host_response_rate, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 2nd variable with missingness\n",
    "print(train['host_response_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1065.000000\n",
       "mean        0.948178\n",
       "std         0.166377\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         1.000000\n",
       "max         1.000000\n",
       "Name: host_response_rate, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['host_response_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by taking column mean\n",
    "train[\"host_response_rate\"].fillna(np.mean(train[\"host_response_rate\"]), inplace=True)\n",
    "test[\"host_response_rate\"].fillna(np.mean(test[\"host_response_rate\"]), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1.00\n",
      "1       0.83\n",
      "2       0.98\n",
      "3       0.97\n",
      "4        NaN\n",
      "        ... \n",
      "1995    0.99\n",
      "1996    0.95\n",
      "1997    0.95\n",
      "1998    0.50\n",
      "1999    0.82\n",
      "Name: host_acceptance_rate, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 3rd variable with missingness\n",
    "print(train['host_acceptance_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1287.000000\n",
       "mean        0.828026\n",
       "std         0.279295\n",
       "min         0.000000\n",
       "25%         0.780000\n",
       "50%         0.970000\n",
       "75%         1.000000\n",
       "max         1.000000\n",
       "Name: host_acceptance_rate, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['host_acceptance_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by taking column mean\n",
    "train[\"host_acceptance_rate\"].fillna(np.mean(train[\"host_acceptance_rate\"]), inplace=True)\n",
    "test[\"host_acceptance_rate\"].fillna(np.mean(test[\"host_acceptance_rate\"]), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0.0\n",
      "1       1.0\n",
      "2       2.0\n",
      "3       1.0\n",
      "4       1.0\n",
      "       ... \n",
      "1995    1.0\n",
      "1996    1.0\n",
      "1997    1.0\n",
      "1998    1.0\n",
      "1999    1.0\n",
      "Name: bedrooms, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 4th variable with missingness\n",
    "print(train['bedrooms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1998.000000\n",
       "mean        1.467467\n",
       "std         0.883665\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max         6.000000\n",
       "Name: bedrooms, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['bedrooms'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by filling NA with a 0 value\n",
    "train[\"bedrooms\"].fillna(0, inplace=True)\n",
    "test[\"bedrooms\"].fillna(0, inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1.0\n",
      "1       2.0\n",
      "2       2.0\n",
      "3       1.0\n",
      "4       1.0\n",
      "       ... \n",
      "1995    1.0\n",
      "1996    1.0\n",
      "1997    1.0\n",
      "1998    1.0\n",
      "1999    0.0\n",
      "Name: beds, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 5th variable with missingness\n",
    "print(train['beds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1988.000000\n",
       "mean        1.830986\n",
       "std         1.259331\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max        16.000000\n",
       "Name: beds, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['beds'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Airbed</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futon</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pull-out Sofa</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Bed</th>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0          count\n",
       "bed_type            \n",
       "Airbed             3\n",
       "Futon              2\n",
       "Pull-out Sofa     10\n",
       "Real Bed        1985"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of bed\n",
    "pd.crosstab(index=train[\"bed_type\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by filling NA with a 0 value\n",
    "train[\"beds\"].fillna(0, inplace=True)\n",
    "test[\"beds\"].fillna(0, inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         NaN\n",
      "1         0.0\n",
      "2         0.0\n",
      "3       500.0\n",
      "4       400.0\n",
      "        ...  \n",
      "1995      NaN\n",
      "1996    250.0\n",
      "1997      NaN\n",
      "1998      0.0\n",
      "1999    250.0\n",
      "Name: security_deposit, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 6th variable with missingness\n",
    "print(train['security_deposit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1293.000000\n",
       "mean      332.248260\n",
       "std       474.553958\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%       200.000000\n",
       "75%       500.000000\n",
       "max      7021.000000\n",
       "Name: security_deposit, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['security_deposit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by filling NA with a 0 value\n",
    "train[\"security_deposit\"].fillna(0, inplace=True)\n",
    "test[\"security_deposit\"].fillna(0, inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean      214.798500\n",
       "std       413.271789\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%       300.000000\n",
       "max      7021.000000\n",
       "Name: security_deposit, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['security_deposit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         NaN\n",
      "1        40.0\n",
      "2        90.0\n",
      "3       250.0\n",
      "4        50.0\n",
      "        ...  \n",
      "1995     20.0\n",
      "1996     35.0\n",
      "1997    100.0\n",
      "1998      0.0\n",
      "1999     30.0\n",
      "Name: cleaning_fee, Length: 2000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Overview the 7th variable with missingness\n",
    "print(train['cleaning_fee'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1477.000000\n",
       "mean       84.099526\n",
       "std        68.092518\n",
       "min         0.000000\n",
       "25%        35.000000\n",
       "50%        70.000000\n",
       "75%       110.000000\n",
       "max       500.000000\n",
       "Name: cleaning_fee, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['cleaning_fee'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by filling NA with a 0 value\n",
    "train[\"cleaning_fee\"].fillna(0, inplace=True)\n",
    "test[\"cleaning_fee\"].fillna(0, inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean       62.107500\n",
       "std        69.210324\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%        50.000000\n",
       "75%       100.000000\n",
       "max       500.000000\n",
       "Name: cleaning_fee, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['cleaning_fee'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>90.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>98.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>87.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      review_scores_rating  review_scores_accuracy  review_scores_cleanliness  \\\n",
       "0                    100.0                    10.0                       10.0   \n",
       "1                    100.0                    10.0                       10.0   \n",
       "2                     92.0                     9.0                        9.0   \n",
       "3                     97.0                    10.0                       10.0   \n",
       "4                      NaN                     NaN                        NaN   \n",
       "...                    ...                     ...                        ...   \n",
       "1995                  90.0                     9.0                        9.0   \n",
       "1996                  95.0                    10.0                        9.0   \n",
       "1997                  98.0                    10.0                       10.0   \n",
       "1998                  87.0                     8.0                        8.0   \n",
       "1999                   NaN                     NaN                        NaN   \n",
       "\n",
       "      review_scores_checkin  review_scores_communication  \\\n",
       "0                       9.0                         10.0   \n",
       "1                      10.0                         10.0   \n",
       "2                      10.0                         10.0   \n",
       "3                      10.0                         10.0   \n",
       "4                       NaN                          NaN   \n",
       "...                     ...                          ...   \n",
       "1995                   10.0                         10.0   \n",
       "1996                   10.0                         10.0   \n",
       "1997                   10.0                         10.0   \n",
       "1998                   10.0                         10.0   \n",
       "1999                    NaN                          NaN   \n",
       "\n",
       "      review_scores_location  review_scores_value  reviews_per_month  \n",
       "0                       10.0                 10.0               0.23  \n",
       "1                       10.0                 10.0               0.83  \n",
       "2                       10.0                  9.0               6.90  \n",
       "3                       10.0                 10.0               1.32  \n",
       "4                        NaN                  NaN               0.07  \n",
       "...                      ...                  ...                ...  \n",
       "1995                    10.0                 10.0               2.67  \n",
       "1996                    10.0                  9.0               5.67  \n",
       "1997                    10.0                 10.0               2.13  \n",
       "1998                     9.0                  9.0               0.16  \n",
       "1999                     NaN                  NaN                NaN  \n",
       "\n",
       "[2000 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The remaining review variables with missingness\n",
    "train[['review_scores_rating', 'review_scores_accuracy',\n",
    "       'review_scores_cleanliness', 'review_scores_checkin',\n",
    "       'review_scores_communication', 'review_scores_location',\n",
    "       'review_scores_value', 'reviews_per_month']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1509.000000\n",
       "mean       92.993373\n",
       "std        10.387133\n",
       "min        20.000000\n",
       "25%        90.000000\n",
       "50%        96.000000\n",
       "75%       100.000000\n",
       "max       100.000000\n",
       "Name: review_scores_rating, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the 8th variable with missingness\n",
    "train['review_scores_rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define scores to star\n",
    "def rating_star(x):\n",
    "    if x == np.nan:\n",
    "        return \"No Rating\"\n",
    "    elif x <= 20:\n",
    "        return '1-star'\n",
    "    elif x <= 40:\n",
    "        return '2-star'\n",
    "    elif x <= 60:\n",
    "        return '3-star'\n",
    "    elif x <= 80:\n",
    "        return '4-star'\n",
    "    else:\n",
    "        return '5-star'\n",
    "\n",
    "# Convert scores range to star rating\n",
    "train['review_scores_rating'] = train['review_scores_rating'].apply(rating_star)\n",
    "test['review_scores_rating'] = test['review_scores_rating'].apply(rating_star)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1506.000000</td>\n",
       "      <td>1509.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>1509.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.518592</td>\n",
       "      <td>9.185553</td>\n",
       "      <td>9.680823</td>\n",
       "      <td>9.709742</td>\n",
       "      <td>9.720637</td>\n",
       "      <td>9.295952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.015488</td>\n",
       "      <td>1.274637</td>\n",
       "      <td>0.845061</td>\n",
       "      <td>0.840324</td>\n",
       "      <td>0.730382</td>\n",
       "      <td>1.033954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       review_scores_accuracy  review_scores_cleanliness  \\\n",
       "count             1506.000000                1509.000000   \n",
       "mean                 9.518592                   9.185553   \n",
       "std                  1.015488                   1.274637   \n",
       "min                  2.000000                   2.000000   \n",
       "25%                  9.000000                   9.000000   \n",
       "50%                 10.000000                  10.000000   \n",
       "75%                 10.000000                  10.000000   \n",
       "max                 10.000000                  10.000000   \n",
       "\n",
       "       review_scores_checkin  review_scores_communication  \\\n",
       "count            1507.000000                  1509.000000   \n",
       "mean                9.680823                     9.709742   \n",
       "std                 0.845061                     0.840324   \n",
       "min                 2.000000                     2.000000   \n",
       "25%                10.000000                    10.000000   \n",
       "50%                10.000000                    10.000000   \n",
       "75%                10.000000                    10.000000   \n",
       "max                10.000000                    10.000000   \n",
       "\n",
       "       review_scores_location  review_scores_value  \n",
       "count             1507.000000          1507.000000  \n",
       "mean                 9.720637             9.295952  \n",
       "std                  0.730382             1.033954  \n",
       "min                  2.000000             2.000000  \n",
       "25%                 10.000000             9.000000  \n",
       "50%                 10.000000            10.000000  \n",
       "75%                 10.000000            10.000000  \n",
       "max                 10.000000            10.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the following 6 review_scores range [2,10]\n",
    "review_outof10_train=train[['review_scores_accuracy',\n",
    "       'review_scores_cleanliness', 'review_scores_checkin',\n",
    "       'review_scores_communication', 'review_scores_location',\n",
    "       'review_scores_value']]\n",
    "\n",
    "review_outof10_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert scores range to star rating\n",
    "review_star = {np.nan:'No Review', 2:'1-star', \n",
    "               3:'2-star', 4:'2-star', \n",
    "               5:'3-star', 6:'3-star', \n",
    "               7:'4-star', 8:'4-star', \n",
    "               9:'5-star', 10:'5-star'}\n",
    "review_outof10_train = review_outof10_train.replace(review_star)\n",
    "\n",
    "review_outof10_test=test[['review_scores_accuracy',\n",
    "       'review_scores_cleanliness', 'review_scores_checkin',\n",
    "       'review_scores_communication', 'review_scores_location',\n",
    "       'review_scores_value']]\n",
    "review_outof10_test = review_outof10_test.replace(review_star)\n",
    "\n",
    "# Store star rating to original data sets\n",
    "train.update(review_outof10_train)\n",
    "test.update(review_outof10_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean        0.869050\n",
       "std         1.406778\n",
       "min         0.000000\n",
       "25%         0.030000\n",
       "50%         0.225000\n",
       "75%         1.050000\n",
       "max        12.860000\n",
       "Name: reviews_per_month, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the 15th variable with missingness\n",
    "train['reviews_per_month'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data by filling NA with a 0 value\n",
    "train[\"reviews_per_month\"].fillna(0, inplace=True)\n",
    "test[\"reviews_per_month\"].fillna(0, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                  0\n",
       "price                               0\n",
       "host_response_time                  0\n",
       "host_response_rate                  0\n",
       "host_acceptance_rate                0\n",
       "host_is_superhost                   0\n",
       "host_listings_count                 0\n",
       "host_identity_verified              0\n",
       "latitude                            0\n",
       "longitude                           0\n",
       "property_type                       0\n",
       "room_type                           0\n",
       "accommodates                        0\n",
       "bathrooms                           0\n",
       "bedrooms                            0\n",
       "beds                                0\n",
       "bed_type                            0\n",
       "security_deposit                    0\n",
       "cleaning_fee                        0\n",
       "guests_included                     0\n",
       "extra_people                        0\n",
       "minimum_nights                      0\n",
       "maximum_nights                      0\n",
       "number_of_reviews                   0\n",
       "review_scores_rating                0\n",
       "review_scores_accuracy              0\n",
       "review_scores_cleanliness           0\n",
       "review_scores_checkin               0\n",
       "review_scores_communication         0\n",
       "review_scores_location              0\n",
       "review_scores_value                 0\n",
       "instant_bookable                    0\n",
       "cancellation_policy                 0\n",
       "require_guest_profile_picture       0\n",
       "require_guest_phone_verification    0\n",
       "reviews_per_month                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all missing values have been processed\n",
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>151.242035</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>within 12 hours</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>151.290979</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>151.072890</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>f</td>\n",
       "      <td>260</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>151.195131</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>within a day</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>151.211849</td>\n",
       "      <td>...</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  price host_response_time  host_response_rate  host_acceptance_rate  \\\n",
       "0  2000    199     within an hour            1.000000              1.000000   \n",
       "1  2001     95    within 12 hours            0.948178              0.830000   \n",
       "2  2002    156     within an hour            0.910000              0.980000   \n",
       "3  2003    100     within an hour            0.990000              0.970000   \n",
       "4  2004    100       within a day            1.000000              0.828026   \n",
       "\n",
       "  host_is_superhost  host_listings_count host_identity_verified   latitude  \\\n",
       "0                 t                    5                      f -33.918732   \n",
       "1                 t                    1                      f -33.698425   \n",
       "2                 f                    8                      f -33.847388   \n",
       "3                 f                  260                      f -33.870261   \n",
       "4                 f                    1                      f -33.908168   \n",
       "\n",
       "    longitude  ... review_scores_cleanliness review_scores_checkin  \\\n",
       "0  151.242035  ...                    5-star                5-star   \n",
       "1  151.290979  ...                    5-star                5-star   \n",
       "2  151.072890  ...                    5-star                5-star   \n",
       "3  151.195131  ...                    5-star                5-star   \n",
       "4  151.211849  ...                 No Review             No Review   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                       5-star                  5-star               5-star   \n",
       "1                       5-star                  5-star               5-star   \n",
       "2                       5-star                  5-star               5-star   \n",
       "3                       5-star                  5-star               5-star   \n",
       "4                    No Review               No Review            No Review   \n",
       "\n",
       "   instant_bookable          cancellation_policy  \\\n",
       "0                 t                     flexible   \n",
       "1                 t                     moderate   \n",
       "2                 f  strict_14_with_grace_period   \n",
       "3                 t  strict_14_with_grace_period   \n",
       "4                 f                     moderate   \n",
       "\n",
       "   require_guest_profile_picture  require_guest_phone_verification  \\\n",
       "0                              f                                 f   \n",
       "1                              f                                 f   \n",
       "2                              f                                 f   \n",
       "3                              f                                 f   \n",
       "4                              f                                 f   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               0.23  \n",
       "1               0.83  \n",
       "2               6.90  \n",
       "3               1.32  \n",
       "4               0.07  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview the processed dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.00\n",
       "mean      158.70\n",
       "std       105.12\n",
       "min        27.00\n",
       "25%        81.00\n",
       "50%       129.00\n",
       "75%       200.00\n",
       "max       550.00\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistic for price\n",
    "train['price'].describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot price distribution\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.hist(train['price'], bins=10, edgecolor = 'k')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Airbnb Price Distribution ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot log price distribution\n",
    "log_y = np.log(train['price'])\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.hist(log_y, bins=10, edgecolor = 'k')\n",
    "plt.xlabel('log(Price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Airbnb Price Logarithm Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.293305</td>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.553877</td>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>within 12 hours</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.049856</td>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>f</td>\n",
       "      <td>260</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>within a day</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>...</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price    Id  price host_response_time  host_response_rate  \\\n",
       "0   5.293305  2000    199     within an hour            1.000000   \n",
       "1   4.553877  2001     95    within 12 hours            0.948178   \n",
       "2   5.049856  2002    156     within an hour            0.910000   \n",
       "3   4.605170  2003    100     within an hour            0.990000   \n",
       "4   4.605170  2004    100       within a day            1.000000   \n",
       "\n",
       "   host_acceptance_rate host_is_superhost  host_listings_count  \\\n",
       "0              1.000000                 t                    5   \n",
       "1              0.830000                 t                    1   \n",
       "2              0.980000                 f                    8   \n",
       "3              0.970000                 f                  260   \n",
       "4              0.828026                 f                    1   \n",
       "\n",
       "  host_identity_verified   latitude  ...  review_scores_cleanliness  \\\n",
       "0                      f -33.918732  ...                     5-star   \n",
       "1                      f -33.698425  ...                     5-star   \n",
       "2                      f -33.847388  ...                     5-star   \n",
       "3                      f -33.870261  ...                     5-star   \n",
       "4                      f -33.908168  ...                  No Review   \n",
       "\n",
       "  review_scores_checkin review_scores_communication  review_scores_location  \\\n",
       "0                5-star                      5-star                  5-star   \n",
       "1                5-star                      5-star                  5-star   \n",
       "2                5-star                      5-star                  5-star   \n",
       "3                5-star                      5-star                  5-star   \n",
       "4             No Review                   No Review               No Review   \n",
       "\n",
       "   review_scores_value  instant_bookable          cancellation_policy  \\\n",
       "0               5-star                 t                     flexible   \n",
       "1               5-star                 t                     moderate   \n",
       "2               5-star                 f  strict_14_with_grace_period   \n",
       "3               5-star                 t  strict_14_with_grace_period   \n",
       "4            No Review                 f                     moderate   \n",
       "\n",
       "  require_guest_profile_picture  require_guest_phone_verification  \\\n",
       "0                             f                                 f   \n",
       "1                             f                                 f   \n",
       "2                             f                                 f   \n",
       "3                             f                                 f   \n",
       "4                             f                                 f   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               0.23  \n",
       "1               0.83  \n",
       "2               6.90  \n",
       "3               1.32  \n",
       "4               0.07  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add log(price) as a new column \n",
    "column_values = pd.Series(log_y)\n",
    "train.insert(loc=0, column='log_price', value=column_values)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4. Predictor Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 38 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   log_price                         2000 non-null   float64\n",
      " 1   Id                                2000 non-null   int64  \n",
      " 2   price                             2000 non-null   int64  \n",
      " 3   host_response_time                2000 non-null   object \n",
      " 4   host_response_rate                2000 non-null   float64\n",
      " 5   host_acceptance_rate              2000 non-null   float64\n",
      " 6   host_is_superhost                 2000 non-null   object \n",
      " 7   host_listings_count               2000 non-null   int64  \n",
      " 8   host_identity_verified            2000 non-null   object \n",
      " 9   latitude                          2000 non-null   float64\n",
      " 10  longitude                         2000 non-null   float64\n",
      " 11  property_type                     2000 non-null   object \n",
      " 12  room_type                         2000 non-null   object \n",
      " 13  accommodates                      2000 non-null   int64  \n",
      " 14  bathrooms                         2000 non-null   float64\n",
      " 15  bedrooms                          2000 non-null   float64\n",
      " 16  beds                              2000 non-null   float64\n",
      " 17  bed_type                          2000 non-null   object \n",
      " 18  security_deposit                  2000 non-null   float64\n",
      " 19  cleaning_fee                      2000 non-null   float64\n",
      " 20  guests_included                   2000 non-null   int64  \n",
      " 21  extra_people                      2000 non-null   int64  \n",
      " 22  minimum_nights                    2000 non-null   int64  \n",
      " 23  maximum_nights                    2000 non-null   int64  \n",
      " 24  number_of_reviews                 2000 non-null   int64  \n",
      " 25  review_scores_rating              2000 non-null   object \n",
      " 26  review_scores_accuracy            2000 non-null   object \n",
      " 27  review_scores_cleanliness         2000 non-null   object \n",
      " 28  review_scores_checkin             2000 non-null   object \n",
      " 29  review_scores_communication       2000 non-null   object \n",
      " 30  review_scores_location            2000 non-null   object \n",
      " 31  review_scores_value               2000 non-null   object \n",
      " 32  instant_bookable                  2000 non-null   object \n",
      " 33  cancellation_policy               2000 non-null   object \n",
      " 34  require_guest_profile_picture     2000 non-null   object \n",
      " 35  require_guest_phone_verification  2000 non-null   object \n",
      " 36  reviews_per_month                 2000 non-null   float64\n",
      " 37  location                          2000 non-null   object \n",
      "dtypes: float64(11), int64(9), object(18)\n",
      "memory usage: 593.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# check train data column types \n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 36 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Id                                2000 non-null   int64  \n",
      " 1   host_response_time                2000 non-null   object \n",
      " 2   host_response_rate                2000 non-null   float64\n",
      " 3   host_acceptance_rate              2000 non-null   float64\n",
      " 4   host_is_superhost                 2000 non-null   object \n",
      " 5   host_listings_count               2000 non-null   int64  \n",
      " 6   host_identity_verified            2000 non-null   object \n",
      " 7   latitude                          2000 non-null   float64\n",
      " 8   longitude                         2000 non-null   float64\n",
      " 9   property_type                     2000 non-null   object \n",
      " 10  room_type                         2000 non-null   object \n",
      " 11  accommodates                      2000 non-null   int64  \n",
      " 12  bathrooms                         2000 non-null   float64\n",
      " 13  bedrooms                          2000 non-null   float64\n",
      " 14  beds                              2000 non-null   float64\n",
      " 15  bed_type                          2000 non-null   object \n",
      " 16  security_deposit                  2000 non-null   float64\n",
      " 17  cleaning_fee                      2000 non-null   float64\n",
      " 18  guests_included                   2000 non-null   int64  \n",
      " 19  extra_people                      2000 non-null   int64  \n",
      " 20  minimum_nights                    2000 non-null   int64  \n",
      " 21  maximum_nights                    2000 non-null   int64  \n",
      " 22  number_of_reviews                 2000 non-null   int64  \n",
      " 23  review_scores_rating              2000 non-null   object \n",
      " 24  review_scores_accuracy            2000 non-null   object \n",
      " 25  review_scores_cleanliness         2000 non-null   object \n",
      " 26  review_scores_checkin             2000 non-null   object \n",
      " 27  review_scores_communication       2000 non-null   object \n",
      " 28  review_scores_location            2000 non-null   object \n",
      " 29  review_scores_value               2000 non-null   object \n",
      " 30  instant_bookable                  2000 non-null   object \n",
      " 31  cancellation_policy               2000 non-null   object \n",
      " 32  require_guest_profile_picture     2000 non-null   object \n",
      " 33  require_guest_phone_verification  2000 non-null   object \n",
      " 34  reviews_per_month                 2000 non-null   float64\n",
      " 35  location                          2000 non-null   object \n",
      "dtypes: float64(10), int64(8), object(18)\n",
      "memory usage: 562.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# check test data column types \n",
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *1.4.1. Numerical Variable*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.293305</td>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>151.242035</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.553877</td>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>151.290979</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.049856</td>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>8</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>151.072890</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>80</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>260</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>151.195131</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>20</td>\n",
       "      <td>1.32</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>1</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>151.211849</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price    Id  price  host_response_rate  host_acceptance_rate  \\\n",
       "0   5.293305  2000    199            1.000000              1.000000   \n",
       "1   4.553877  2001     95            0.948178              0.830000   \n",
       "2   5.049856  2002    156            0.910000              0.980000   \n",
       "3   4.605170  2003    100            0.990000              0.970000   \n",
       "4   4.605170  2004    100            1.000000              0.828026   \n",
       "\n",
       "   host_listings_count   latitude   longitude  accommodates  bathrooms  \\\n",
       "0                    5 -33.918732  151.242035             2        1.0   \n",
       "1                    1 -33.698425  151.290979             4        1.0   \n",
       "2                    8 -33.847388  151.072890             4        1.0   \n",
       "3                  260 -33.870261  151.195131             2        1.0   \n",
       "4                    1 -33.908168  151.211849             1        1.0   \n",
       "\n",
       "   bedrooms  beds  security_deposit  cleaning_fee  guests_included  \\\n",
       "0       0.0   1.0               0.0           0.0                1   \n",
       "1       1.0   2.0               0.0          40.0                1   \n",
       "2       2.0   2.0               0.0          90.0                4   \n",
       "3       1.0   1.0             500.0         250.0                2   \n",
       "4       1.0   1.0             400.0          50.0                1   \n",
       "\n",
       "   extra_people  minimum_nights  maximum_nights  number_of_reviews  \\\n",
       "0             0               1            1125                  3   \n",
       "1             7               2              45                 18   \n",
       "2            30               1            1125                 80   \n",
       "3             0               2            1125                 20   \n",
       "4             0               3               3                  1   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               0.23  \n",
       "1               0.83  \n",
       "2               6.90  \n",
       "3               1.32  \n",
       "4               0.07  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all the numerical cloumns from train dataset\n",
    "numerical_data=train.select_dtypes(['int64','float64'])\n",
    "numerical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>80</td>\n",
       "      <td>6.90</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>100</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>260</td>\n",
       "      <td>-33.870261</td>\n",
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       "      <td>250.0</td>\n",
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       "      <td>20</td>\n",
       "      <td>1.32</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>400.0</td>\n",
       "      <td>50.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.07</td>\n",
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      ],
      "text/plain": [
       "   log_price  price  host_response_rate  host_acceptance_rate  \\\n",
       "0   5.293305    199            1.000000              1.000000   \n",
       "1   4.553877     95            0.948178              0.830000   \n",
       "2   5.049856    156            0.910000              0.980000   \n",
       "3   4.605170    100            0.990000              0.970000   \n",
       "4   4.605170    100            1.000000              0.828026   \n",
       "\n",
       "   host_listings_count   latitude   longitude  accommodates  bathrooms  \\\n",
       "0                    5 -33.918732  151.242035             2        1.0   \n",
       "1                    1 -33.698425  151.290979             4        1.0   \n",
       "2                    8 -33.847388  151.072890             4        1.0   \n",
       "3                  260 -33.870261  151.195131             2        1.0   \n",
       "4                    1 -33.908168  151.211849             1        1.0   \n",
       "\n",
       "   bedrooms  beds  security_deposit  cleaning_fee  guests_included  \\\n",
       "0       0.0   1.0               0.0           0.0                1   \n",
       "1       1.0   2.0               0.0          40.0                1   \n",
       "2       2.0   2.0               0.0          90.0                4   \n",
       "3       1.0   1.0             500.0         250.0                2   \n",
       "4       1.0   1.0             400.0          50.0                1   \n",
       "\n",
       "   extra_people  minimum_nights  maximum_nights  number_of_reviews  \\\n",
       "0             0               1            1125                  3   \n",
       "1             7               2              45                 18   \n",
       "2            30               1            1125                 80   \n",
       "3             0               2            1125                 20   \n",
       "4             0               3               3                  1   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               0.23  \n",
       "1               0.83  \n",
       "2               6.90  \n",
       "3               1.32  \n",
       "4               0.07  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Id column from numerical columns of train dataset\n",
    "new_numerical_data =  numerical_data.drop(['Id'], axis=1)\n",
    "new_numerical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>3</td>\n",
       "      <td>1125</td>\n",
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       "      <th>2</th>\n",
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       "      <td>90</td>\n",
       "      <td>300</td>\n",
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       "      <td>1.18</td>\n",
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       "      <td>151.178277</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>-34.044786</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1125</td>\n",
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      "text/plain": [
       "   Id  host_response_rate  host_acceptance_rate  host_listings_count  \\\n",
       "0   0            1.000000              0.570000                    1   \n",
       "1   1            0.000000              1.000000                    1   \n",
       "2   2            0.938955              1.000000                    1   \n",
       "3   3            0.938955              0.819867                    2   \n",
       "4   4            1.000000              0.850000                    1   \n",
       "\n",
       "    latitude   longitude  accommodates  bathrooms  bedrooms  beds  \\\n",
       "0 -33.776680  151.261740             1        1.0       1.0   1.0   \n",
       "1 -33.795694  151.155964             4        1.0       2.0   2.0   \n",
       "2 -33.874641  151.225248             2        1.0       0.0   1.0   \n",
       "3 -33.898198  151.178277             2        1.0       1.0   1.0   \n",
       "4 -34.044786  151.145606             2        1.0       1.0   1.0   \n",
       "\n",
       "   security_deposit  cleaning_fee  guests_included  extra_people  \\\n",
       "0               0.0           0.0                1             0   \n",
       "1             500.0          50.0                1             0   \n",
       "2             800.0          80.0                1             0   \n",
       "3               0.0           0.0                1             0   \n",
       "4               0.0          25.0                1             0   \n",
       "\n",
       "   minimum_nights  maximum_nights  number_of_reviews  reviews_per_month  \n",
       "0               2              60                  1               0.39  \n",
       "1               3            1125                  1               0.07  \n",
       "2              90             300                 46               1.18  \n",
       "3               2              14                 10               0.37  \n",
       "4               3            1125                 19               1.30  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all the numerical cloumns from test dataset\n",
    "test_numerical_data=test.select_dtypes(['int64','float64'])\n",
    "test_numerical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
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       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>0.37</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   host_response_rate  host_acceptance_rate  host_listings_count   latitude  \\\n",
       "0            1.000000              0.570000                    1 -33.776680   \n",
       "1            0.000000              1.000000                    1 -33.795694   \n",
       "2            0.938955              1.000000                    1 -33.874641   \n",
       "3            0.938955              0.819867                    2 -33.898198   \n",
       "4            1.000000              0.850000                    1 -34.044786   \n",
       "\n",
       "    longitude  accommodates  bathrooms  bedrooms  beds  security_deposit  \\\n",
       "0  151.261740             1        1.0       1.0   1.0               0.0   \n",
       "1  151.155964             4        1.0       2.0   2.0             500.0   \n",
       "2  151.225248             2        1.0       0.0   1.0             800.0   \n",
       "3  151.178277             2        1.0       1.0   1.0               0.0   \n",
       "4  151.145606             2        1.0       1.0   1.0               0.0   \n",
       "\n",
       "   cleaning_fee  guests_included  extra_people  minimum_nights  \\\n",
       "0           0.0                1             0               2   \n",
       "1          50.0                1             0               3   \n",
       "2          80.0                1             0              90   \n",
       "3           0.0                1             0               2   \n",
       "4          25.0                1             0               3   \n",
       "\n",
       "   maximum_nights  number_of_reviews  reviews_per_month  \n",
       "0              60                  1               0.39  \n",
       "1            1125                  1               0.07  \n",
       "2             300                 46               1.18  \n",
       "3              14                 10               0.37  \n",
       "4            1125                 19               1.30  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Id column from numerical columns of test dataset\n",
    "new_test_numerical_data = test_numerical_data.drop(['Id'], axis=1)\n",
    "new_test_numerical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## heatmap:correlation between price and other numerical variables\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(new_numerical_data.corr(),annot=True,linewidth=0.5,cmap='autumn',fmt='.2f')\n",
    "plt.title('Correlations')\n",
    "plt.savefig('correlation.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the independent variables with a price correlation greater than 0.5\n",
    "indep_variables = [\"accommodates\", \"beds\", \"bedrooms\", \"cleaning_fee\"]\n",
    "dep_variable = ['log_price']    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.00000\n",
       "mean        3.15300\n",
       "std         1.86476\n",
       "min         1.00000\n",
       "25%         2.00000\n",
       "50%         2.00000\n",
       "75%         4.00000\n",
       "max        16.00000\n",
       "Name: accommodates, dtype: float64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploratory Data Analysis: statistics\n",
    "train['accommodates'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis: regressionplot\n",
    "sns.regplot(y=train[dep_variable], x=train[indep_variables[0]], data=train) \n",
    "plt.xlabel(str([indep_variables[0]]))\n",
    "plt.ylabel(str([dep_variable[0]]))\n",
    "plt.title(\"Training: log(Price) vs \"+str([indep_variables[0]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean        1.820000\n",
       "std         1.263487\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max        16.000000\n",
       "Name: beds, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['beds'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y=train[dep_variable], x=train[indep_variables[1]], data=train) \n",
    "plt.xlabel(str([indep_variables[1]]))\n",
    "plt.ylabel(str([dep_variable[0]]))\n",
    "plt.title(\"Training: log(Price) vs \"+str([indep_variables[1]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean        1.466000\n",
       "std         0.884441\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max         6.000000\n",
       "Name: bedrooms, dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['bedrooms'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y=train[dep_variable], x=train[indep_variables[2]], data=train) \n",
    "plt.xlabel(str([indep_variables[2]]))\n",
    "plt.ylabel(str([dep_variable[0]]))\n",
    "plt.title(\"Training: log(Price) vs \"+str([indep_variables[2]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean       62.107500\n",
       "std        69.210324\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%        50.000000\n",
       "75%       100.000000\n",
       "max       500.000000\n",
       "Name: cleaning_fee, dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['cleaning_fee'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y=train[dep_variable], x=train[indep_variables[3]], data=train) \n",
    "plt.xlabel(str([indep_variables[3]]))\n",
    "plt.ylabel(str([dep_variable[0]]))\n",
    "plt.title(\"Training: log(Price) vs \"+str([indep_variables[3]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 3)\n",
    "data = train[['latitude','longitude']]\n",
    "kmeans.fit(data)\n",
    "labels = kmeans.labels_\n",
    "centroids = kmeans.cluster_centers_\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "price = train ['log_price']\n",
    "normalize= (price-price.min())/(price.max()-price.min())  #normalization the price with an interval between 0 and 1\n",
    "geo_price= plt.scatter(x=train['latitude'], y= train['longitude'], \n",
    "                       c=normalize,s=40, vmin =0, vmax=1, cmap='coolwarm')\n",
    "plt.scatter(train['latitude'], train['longitude'],c=normalize, s = 20, label = 'Data', cmap = 'coolwarm')\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], s = 100, color = 'gold', edgecolors = 'black', label = 'Centroids')\n",
    "plt.colorbar(geo_price)\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Longitude')\n",
    "plt.title('The relationship between grographic location and natual logarithm price with KMeans model in trainning set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-33.88742647 151.11690382]\n",
      " [-33.88685609 151.22734122]\n",
      " [-33.74619737 151.27030441]]\n"
     ]
    }
   ],
   "source": [
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-33.88742647, 151.11690382]),\n",
       " array([-33.88685609, 151.22734122]),\n",
       " array([-33.74619737, 151.27030441]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix names to centroids\n",
    "range = [0,1,2]\n",
    "for n in range:\n",
    "    if centroids[n,1]== centroids[:,1].min(): #with the smallest longtitude\n",
    "        below=centroids[n]\n",
    "    elif centroids[n,1]== centroids[:,1].max(): #with the largest longtitude\n",
    "        above=centroids[n]\n",
    "    else:\n",
    "        average=centroids[n]\n",
    "below,average, above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate euclidean distance to 3 centroids\n",
    "dist_to_below = np.sqrt((train['latitude']-below[0])**2 + (train['longitude']-below[1])**2)\n",
    "dist_to_average = np.sqrt((train['latitude']-average[0])**2 + (train['longitude']-average[1])**2)\n",
    "dist_to_above = np.sqrt((train['latitude']-above[0])**2 + (train['longitude']-above[1])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column with geographic info\n",
    "dist=[dist_to_below,dist_to_average,dist_to_above]\n",
    "\n",
    "x_list=[]\n",
    "i = 0\n",
    "while i<2000:\n",
    "    x_list.append(i)\n",
    "    i += 1\n",
    "\n",
    "location =[]\n",
    "for index in x_list:\n",
    "    if dist_to_below[index] == min(dist_to_below[index],dist_to_average[index],dist_to_above[index]):\n",
    "        location.append('below_average')\n",
    "    elif dist_to_below[index] == max(dist_to_below[index],dist_to_average[index],dist_to_above[index]):\n",
    "        location.append('above_average')  \n",
    "    else:\n",
    "        location.append('average')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>above_average</th>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average</th>\n",
       "      <td>1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below_average</th>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0          count\n",
       "location            \n",
       "above_average    568\n",
       "average         1075\n",
       "below_average    357"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['location']=location\n",
    "pd.crosstab(index=train[\"location\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.293305</td>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.23</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.553877</td>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>within 12 hours</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.83</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.049856</td>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>6.90</td>\n",
       "      <td>below_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>f</td>\n",
       "      <td>260</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.32</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>within a day</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>...</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.07</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price    Id  price host_response_time  host_response_rate  \\\n",
       "0   5.293305  2000    199     within an hour            1.000000   \n",
       "1   4.553877  2001     95    within 12 hours            0.948178   \n",
       "2   5.049856  2002    156     within an hour            0.910000   \n",
       "3   4.605170  2003    100     within an hour            0.990000   \n",
       "4   4.605170  2004    100       within a day            1.000000   \n",
       "\n",
       "   host_acceptance_rate host_is_superhost  host_listings_count  \\\n",
       "0              1.000000                 t                    5   \n",
       "1              0.830000                 t                    1   \n",
       "2              0.980000                 f                    8   \n",
       "3              0.970000                 f                  260   \n",
       "4              0.828026                 f                    1   \n",
       "\n",
       "  host_identity_verified   latitude  ...  review_scores_checkin  \\\n",
       "0                      f -33.918732  ...                 5-star   \n",
       "1                      f -33.698425  ...                 5-star   \n",
       "2                      f -33.847388  ...                 5-star   \n",
       "3                      f -33.870261  ...                 5-star   \n",
       "4                      f -33.908168  ...              No Review   \n",
       "\n",
       "  review_scores_communication review_scores_location  review_scores_value  \\\n",
       "0                      5-star                 5-star               5-star   \n",
       "1                      5-star                 5-star               5-star   \n",
       "2                      5-star                 5-star               5-star   \n",
       "3                      5-star                 5-star               5-star   \n",
       "4                   No Review              No Review            No Review   \n",
       "\n",
       "   instant_bookable          cancellation_policy  \\\n",
       "0                 t                     flexible   \n",
       "1                 t                     moderate   \n",
       "2                 f  strict_14_with_grace_period   \n",
       "3                 t  strict_14_with_grace_period   \n",
       "4                 f                     moderate   \n",
       "\n",
       "   require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                              f                                f   \n",
       "1                              f                                f   \n",
       "2                              f                                f   \n",
       "3                              f                                f   \n",
       "4                              f                                f   \n",
       "\n",
       "   reviews_per_month       location  \n",
       "0               0.23        average  \n",
       "1               0.83  above_average  \n",
       "2               6.90  below_average  \n",
       "3               1.32        average  \n",
       "4               0.07        average  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Repeat the above steps to generate \"location\" list in test set\n",
    "data_test = test[['latitude','longitude']].to_numpy()\n",
    "kmeans = KMeans(n_clusters = 3)\n",
    "kmeans.fit(data_test)\n",
    "labels_test = kmeans.labels_\n",
    "centroids_test = kmeans.cluster_centers_\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.scatter(data_test[:,0], data_test[:,1],s = 20, label = 'Data', cmap = 'rainbow', c = labels_test)\n",
    "plt.scatter(centroids_test[:, 0], centroids_test[:, 1], s = 100, color = 'gold', edgecolors = 'black', label = 'Centroids')\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Longitude')\n",
    "plt.title('grographic location overview with KMeans model in test set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-33.85573831, 151.11751964]),\n",
       " array([-33.89149818, 151.22317006]),\n",
       " array([-33.74790241, 151.28154856]))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_range = [0,1,2]\n",
    "for t in test_range:\n",
    "    if centroids_test[t,1]== centroids_test[:,1].min(): #with the smallest longtitude\n",
    "        below_test=centroids_test[t]\n",
    "    elif centroids_test[t,1]== centroids_test[:,1].max(): #with the largest longtitude\n",
    "        above_test=centroids_test[t]\n",
    "    else:\n",
    "        average_test=centroids_test[t]\n",
    "below_test,average_test, above_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_to_below_test = np.sqrt((test['latitude']-below_test[0])**2 + (test['longitude']-below_test[1])**2)\n",
    "dist_to_average_test = np.sqrt((test['latitude']-average_test[0])**2 + (test['longitude']-average_test[1])**2)\n",
    "dist_to_above_test = np.sqrt((test['latitude']-above_test[0])**2 + (test['longitude']-above_test[1])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_test=[dist_to_below_test,dist_to_average_test,dist_to_above_test]\n",
    "\n",
    "x_list_test=[]\n",
    "i_test = 0\n",
    "while i_test<2000:\n",
    "    x_list_test.append(i_test)\n",
    "    i_test += 1\n",
    "\n",
    "location_test =[]\n",
    "for index_test in x_list_test:\n",
    "    if dist_to_below_test[index_test] == min(dist_to_below_test[index_test],dist_to_average_test[index_test],dist_to_above_test[index_test]):\n",
    "        location_test.append('below_average')\n",
    "    elif dist_to_below_test[index_test] == max(dist_to_below_test[index_test],dist_to_average_test[index_test],dist_to_above_test[index_test]):\n",
    "        location_test.append('above_average')  \n",
    "    else:\n",
    "        location_test.append('average')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>Id</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.293305</td>\n",
       "      <td>2000</td>\n",
       "      <td>199</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.23</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.553877</td>\n",
       "      <td>2001</td>\n",
       "      <td>95</td>\n",
       "      <td>within 12 hours</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.83</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.049856</td>\n",
       "      <td>2002</td>\n",
       "      <td>156</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>6.90</td>\n",
       "      <td>below_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2003</td>\n",
       "      <td>100</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>f</td>\n",
       "      <td>260</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>...</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.32</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>2004</td>\n",
       "      <td>100</td>\n",
       "      <td>within a day</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>...</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.07</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price    Id  price host_response_time  host_response_rate  \\\n",
       "0   5.293305  2000    199     within an hour            1.000000   \n",
       "1   4.553877  2001     95    within 12 hours            0.948178   \n",
       "2   5.049856  2002    156     within an hour            0.910000   \n",
       "3   4.605170  2003    100     within an hour            0.990000   \n",
       "4   4.605170  2004    100       within a day            1.000000   \n",
       "\n",
       "   host_acceptance_rate host_is_superhost  host_listings_count  \\\n",
       "0              1.000000                 t                    5   \n",
       "1              0.830000                 t                    1   \n",
       "2              0.980000                 f                    8   \n",
       "3              0.970000                 f                  260   \n",
       "4              0.828026                 f                    1   \n",
       "\n",
       "  host_identity_verified   latitude  ...  review_scores_checkin  \\\n",
       "0                      f -33.918732  ...                 5-star   \n",
       "1                      f -33.698425  ...                 5-star   \n",
       "2                      f -33.847388  ...                 5-star   \n",
       "3                      f -33.870261  ...                 5-star   \n",
       "4                      f -33.908168  ...              No Review   \n",
       "\n",
       "  review_scores_communication review_scores_location  review_scores_value  \\\n",
       "0                      5-star                 5-star               5-star   \n",
       "1                      5-star                 5-star               5-star   \n",
       "2                      5-star                 5-star               5-star   \n",
       "3                      5-star                 5-star               5-star   \n",
       "4                   No Review              No Review            No Review   \n",
       "\n",
       "   instant_bookable          cancellation_policy  \\\n",
       "0                 t                     flexible   \n",
       "1                 t                     moderate   \n",
       "2                 f  strict_14_with_grace_period   \n",
       "3                 t  strict_14_with_grace_period   \n",
       "4                 f                     moderate   \n",
       "\n",
       "   require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                              f                                f   \n",
       "1                              f                                f   \n",
       "2                              f                                f   \n",
       "3                              f                                f   \n",
       "4                              f                                f   \n",
       "\n",
       "   reviews_per_month       location  \n",
       "0               0.23        average  \n",
       "1               0.83  above_average  \n",
       "2               6.90  below_average  \n",
       "3               1.32        average  \n",
       "4               0.07        average  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['location']=location_test\n",
    "pd.crosstab(index=test[\"location\"], columns=\"count\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *1.4.2. Categorical Variables*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>Boutique hotel</td>\n",
       "      <td>Hotel room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>within 12 hours</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>Guest suite</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>below_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>5-star</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>within a day</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>5-star</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>No Review</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  host_response_time host_is_superhost host_identity_verified   property_type  \\\n",
       "0     within an hour                 t                      f  Boutique hotel   \n",
       "1    within 12 hours                 t                      f     Guest suite   \n",
       "2     within an hour                 f                      f       Apartment   \n",
       "3     within an hour                 f                      f       Apartment   \n",
       "4       within a day                 f                      f       Apartment   \n",
       "\n",
       "         room_type  bed_type review_scores_rating review_scores_accuracy  \\\n",
       "0       Hotel room  Real Bed               5-star                 5-star   \n",
       "1  Entire home/apt  Real Bed               5-star                 5-star   \n",
       "2  Entire home/apt  Real Bed               5-star                 5-star   \n",
       "3  Entire home/apt  Real Bed               5-star                 5-star   \n",
       "4     Private room  Real Bed               5-star              No Review   \n",
       "\n",
       "  review_scores_cleanliness review_scores_checkin review_scores_communication  \\\n",
       "0                    5-star                5-star                      5-star   \n",
       "1                    5-star                5-star                      5-star   \n",
       "2                    5-star                5-star                      5-star   \n",
       "3                    5-star                5-star                      5-star   \n",
       "4                 No Review             No Review                   No Review   \n",
       "\n",
       "  review_scores_location review_scores_value instant_bookable  \\\n",
       "0                 5-star              5-star                t   \n",
       "1                 5-star              5-star                t   \n",
       "2                 5-star              5-star                f   \n",
       "3                 5-star              5-star                t   \n",
       "4              No Review           No Review                f   \n",
       "\n",
       "           cancellation_policy require_guest_profile_picture  \\\n",
       "0                     flexible                             f   \n",
       "1                     moderate                             f   \n",
       "2  strict_14_with_grace_period                             f   \n",
       "3  strict_14_with_grace_period                             f   \n",
       "4                     moderate                             f   \n",
       "\n",
       "  require_guest_phone_verification       location  \n",
       "0                                f        average  \n",
       "1                                f  above_average  \n",
       "2                                f  below_average  \n",
       "3                                f        average  \n",
       "4                                f        average  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all the categorical cloumns\n",
    "categorical_data=train.select_dtypes(['object'])\n",
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_response_time                   5\n",
       "host_is_superhost                    2\n",
       "host_identity_verified               2\n",
       "property_type                       23\n",
       "room_type                            4\n",
       "bed_type                             4\n",
       "review_scores_rating                 5\n",
       "review_scores_accuracy               6\n",
       "review_scores_cleanliness            6\n",
       "review_scores_checkin                6\n",
       "review_scores_communication          6\n",
       "review_scores_location               6\n",
       "review_scores_value                  6\n",
       "instant_bookable                     2\n",
       "cancellation_policy                  5\n",
       "require_guest_profile_picture        2\n",
       "require_guest_phone_verification     2\n",
       "location                             3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the categorical cloumns\n",
    "categorical_data= train.columns[train.dtypes=='object']\n",
    "train[categorical_data].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartment</th>\n",
       "      <td>1327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bed and breakfast</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boat</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boutique hotel</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bungalow</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camper/RV</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condominium</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cottage</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farm stay</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guest suite</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guesthouse</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hostel</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House</th>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loft</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serviced apartment</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tent</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiny house</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Townhouse</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treehouse</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villa</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0               count\n",
       "property_type            \n",
       "Apartment            1327\n",
       "Bed and breakfast       2\n",
       "Boat                    2\n",
       "Boutique hotel         10\n",
       "Bungalow                6\n",
       "Cabin                   6\n",
       "Camper/RV               1\n",
       "Condominium            34\n",
       "Cottage                 7\n",
       "Farm stay               1\n",
       "Guest suite            34\n",
       "Guesthouse             28\n",
       "Hostel                  3\n",
       "Hotel                   2\n",
       "House                 423\n",
       "Loft                    9\n",
       "Other                   2\n",
       "Serviced apartment      9\n",
       "Tent                    1\n",
       "Tiny house              2\n",
       "Townhouse              78\n",
       "Treehouse               1\n",
       "Villa                  12"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# property type\n",
    "# frequency count for property type\n",
    "pd.crosstab(index=train[\"property_type\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping all the smaller categorical variables for property type except house,townhouse and apartment\n",
    "train['property_type'] = np.where(train['property_type'].str.contains('House'), \"House\", (np.where(train['property_type'].str.contains('Apartment'), \"Apartment\", (np.where(train['property_type'].str.contains('Townhouse'), \"Townhouse\", \"Other\")))))\n",
    "test['property_type'] = np.where(test['property_type'].str.contains('House'), \"House\", (np.where(test['property_type'].str.contains('Apartment'), \"Apartment\", (np.where(test['property_type'].str.contains('Townhouse'), \"Townhouse\", \"Other\")))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartment</th>\n",
       "      <td>1327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House</th>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Townhouse</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0          count\n",
       "property_type       \n",
       "Apartment       1327\n",
       "House            423\n",
       "Other            172\n",
       "Townhouse         78"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count frequency of all categories after grouping\n",
    "pd.crosstab(index=train[\"property_type\"], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='property_type',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('property_type')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs property_type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# room type\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='room_type',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('room_type')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs room_type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#host_response_time\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='host_response_time',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('host_response_time')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs host_response_time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#host_is_superhost\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='host_is_superhost',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('host_is_superhost')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs host_is_superhost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#host_identity_verified\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='host_identity_verified',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('host_identity_verified')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs host_identity_verified')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NYd26dX2XIKkHBmRJ0rQ0HUZMhy+DuWzZsp4rkTSVnIMsSZIkdRiQJUmSpA4DsiRJktRhQJYkSZI6DMiSJElShwFZkiRJ6jAgS5IkSR0GZEmSJKnDgCxJkiR1GJAlSZKkDgOyJEmS1GFAliRJkjoMyJIkSVKHAVmSJEnqMCBLkiRJHQZkSZIkqcOALEmSJHUMPCAnuT7Jz5JcluTiUdYnyfFJhpJckeS5g65JkiRJGsucKdrPS6vq5jHWvRLYvf3ZG/hU+6ekKbR06VKGhoZ6rWHVqlUALFiwoNc6Fi5cyJIlS3qtQZLUn3GPILcjvW9O8v72+S5J9pqEGl4NfK4aFwLbJNlhEvqVNMOsW7eOdevW9V2GJGmWm8gI8ieBB4H9geOAO4HTgOdvYLsCvpukgE9X1Ykj1u8ErOw8X9UuW91tlOQo4CiAXXbZZQJlSxqP6TBiunjxYgCWLVvWcyWSpNlsInOQ966qY4C7AarqNmCzcWz3oqp6Ls1UimOSvGTE+oyyTT1iQdWJVbWoqhbNnz9/AmVLkiRJ4zeRgHxfkk1pw2uS+TQjyutVVTe0f94EfAMYOS1jFbBz5/kC4IYJ1CVJkiRNmolMsTieJuA+KcnfAocCf7m+DZJsCWxSVXe2j19BMz2j63RgcZIv05yc9+uqWo0kqTfT4aTN6WD58uXAQ9N/ZjNPXtVsMu6AXFVfSHIJ8DKaaRGvqaqfb2CzJwPfSDK8ry9W1dlJjm77PAE4CzgIGAJ+Axw54VchSZpUQ0NDXHbl1Wz2xO37LqVX9z/QzAK8+pe39lxJv+697ca+S5Cm1LgDcpIXAFdV1Sfa51sn2buqfjzWNlV1LfB7oyw/ofO4gGMmVLUkaeA2e+L27PDyI/ouQ9PA6u+d3HcJ0pSayBzkTwFrO8/vapdJkiRJG42JBOS0o70AVNWDTN2NRiRJkqQpMZGAfG2SY5PMbX/+FLh2UIVJkiRJfZhIQD4aeCHwS5pLs+1Ne+MOSZIkaWMxkatY3AQcPsBaJEmSpN5tMCAneW9VfSjJxxn9DnfHDqQySZIkqQfjGUEevtbxxYMsZCqtWrWKtXfdxUeOe1/fpWgaWLniWrbacsu+y5AkSdPEBgNyVZ3R3mL62VX1nimoSZIkSerNuOYgV9UDSZ436GKmyoIFC7jr7vt49/v/ru9SNA185Lj3seXmc/suQ5IkTRMTuY7xT5OcDnyN5iYhAFTV1ye9KmmWWbp0KUNDQ32X0bvly5cDsHjx4p4r6d/ChQtZsmRJ32VI0qw0kYC8LXALsH9nWQEGZOkxGhoa4qqfX8O2O+zcdym9enCT5iNp9e13baDlxu3W1Sv7LkGSZrWJXObtyEEWIs122+6wMwe/02n+gjM//eG+S5CkWW3cNwpJ8tQkZyRZk+SmJN9K8pRBFidJkiRNtYncSe+LwFeBHYAdaeYif3kQRUmSJEl9mUhATlV9vqrub39OYZQbh0iSJEkz2URO0vt+kvfRjBoXcBhwZpJtAarq1gHUJ0mSJE2piQTkw9o/3zli+dtoAvNTJ6UiSZIkqUcTuYrFek/IS/Jfqurcx16SJEmS1J+JzEHekL+fxL4kSZKkXkxmQM4k9iVJkiT1YjIDsle0kCRJ0ow3mQFZkiRJmvEmMyBfP4l9SZIkSb0Y91UskrxulMW/Bn5WVTdV1WjrJUmSpBllItdBfjuwD/D99vl+wIXA05McV1Wfn+TaJEmSpCk3kYD8IPCfqupXAEmeDHwK2Bv4IWBAlh6lVatWcfudaznz0x/uuxRNA7esXskDa7fquwxJmrUmMgd5t+Fw3LoJeHp7i+n7JrcsSZIkqR8TGUE+P8m3ga+1zw8FfphkS+D2yS5Mmk0WLFjAprffxcHvfE/fpWgaOPPTH2aHbbbsuwxJmrUmEpCPAV4H7EtzU5DPAqdVVQEvHUBtkiRJ0pQbd0CuqkpyAXAvzU1BLmrDsSRpI7Nq1Sruvf0OVn/v5L5L0TRw7203sqp+03cZ0pQZ9xzkJG8ALqKZWvEG4MdJDh1UYZIkSVIfJjLF4n8Cz6+qmwCSzAe+B5w6iMIkSf1ZsGABd+RWdnj5EX2Xomlg9fdOZsFO2/ZdhjRlJnIVi02Gw3HrlgluL0mSJE17ExlBPjvJOcCX2ueHAWdNfkmSJElSfyZykt57kvwh8CKaq1icWFXfGFhlkiRJUg8mMoJMVZ0GnDagWiRJkqTebTAgJ7mT5rJuj1hFc/W3J0x6VZIkSVJPNhiQq2rrx7qTJJsCFwO/rKpDRqzbD/gWcF276OtVddxj3ackSZL0aExoisVj8KfAz4GxRpvPHxmcJUmSpD4M/DJtSRYABwOfGfS+JEmSpMdqKq5jvBR4L/Dgetrsk+TyJN9J8qwpqEmSJEka1UCnWCQ5BLipqi5p5xqP5lJg16pam+Qg4JvA7qP0dRRwFMAuu+wykHqlPt26eiVnfvrDfZfRqztuae5F9ITtntRzJf26dfVKdthmj77LkDTC0qVLGRoa6rWGVatWAc3dLvu0cOFClixZ0msNgzToOcgvAv6gDb6bA09IckpVvXm4QVXd0Xl8VpJPJplXVTd3O6qqE4ETARYtWjTaVTWkGWvhwoV9lzAtrF1zPwA7bLNlz5X0a4dt9vCYkDSqdevW9V3CrDDQgFxVfw78Ofz2ahXv7objdvn2wK+qqpLsRTPt45ZB1iVNNxvzb+ETsXjxYgCWLVvWcyWS9EjT4bPaz8mpMVVXsXiYJEcDVNUJwKHAnyS5H1gHHF5VjhBLkiSpF1MWkKvqPOC89vEJneXLAH8NkiRJ0rQwFVexkCRJkmYMA7IkSZLUYUCWJEmSOno5SW86WLniOj5y3Pv6LqNXN924GoAnbb9Dz5X0a+WK69jjGU/vuwxJkjRNzMqA7PVFG/ffdw8AW24+t+dK+rXHM57uMSGN4t7bbmT1907uu4xe3X/nrQDM2Xrbnivp17233Qg7ze73QLPLrAzI0+E6htOB11KUNBZ/aWwsX95cln/32R4Od9rWY0KzyqwMyJKk9XMgoeFAgjQ7eZKeJEmS1GFAliRJkjoMyJIkSVKHAVmSJEnqMCBLkiRJHV7FQhIAS5cuZWhoqNcali9fDjx05YC+LFy40Ks4SNIsZkCWNG1sscUWfZcgSZIBWVLDEVNJ09l0+JZrOpgu37RNB4P8ts+ALEmSpr2hoSEuufxKHth8dt/VcJN7HwTgol/c0HMl/dr07lsH2r8BWZIkzQgPbL4ta3c9sO8yNA1steLsgfbvVSwkSZKkDkeQJUnT0nSYczpd5nt6ZRVpahmQJUkag1dWkWYnA7IkaVpyxFRSX5yDLEmSJHUYkCVJkqQOA7IkSZLUYUCWJEmSOgzIkiRJUocBWZIkSeowIEuSJEkdBmRJkiSpw4AsSZIkdRiQJUmSpA4DsiRJktRhQJYkSZI65vRdgCRJ0oasWrWKTe++na1WnN13KZoGNr37VlatenBg/TuCLEmSJHU4gixJkqa9BQsWcMNdm7B21wP7LkXTwFYrzmbBgh0H1r8jyJIkSVKHAVmSJEnqmJKAnGTTJD9N8u1R1iXJ8UmGklyR5LlTUZMkSZI0mqkaQf5T4OdjrHslsHv7cxTwqSmqSZIkSXqEgQfkJAuAg4HPjNHk1cDnqnEhsE2SHQZdlyRJkjSaqRhBXgq8FxjrYnU7ASs7z1e1yx4myVFJLk5y8Zo1aya9SEmSJAkGHJCTHALcVFWXrK/ZKMvqEQuqTqyqRVW1aP78+ZNWoyRJktQ16BHkFwF/kOR64MvA/klOGdFmFbBz5/kC4IYB1yVJkiSNaqABuar+vKoWVNVuwOHAv1TVm0c0Ox14S3s1ixcAv66q1YOsS5IkSRpLL3fSS3I0QFWdAJwFHAQMAb8BjuyjJkmSNL1tevetbLXi7L7L6NUm994JwIObbd1zJf3a9O5bgcHdSW/KAnJVnQec1z4+obO8gGOmqg5JkjTzLFy4sO8SpoXly5cDsPvugwuHM8OOAz0mehlBliRJmoglS5b0XcK0sHjxYgCWLVvWcyUbN281LUmSJHUYkCVJkqQOA7IkSZLUYUCWJEmSOgzIkiRJUocBWZIkSeowIEuSJEkdBmRJkiSpw4AsSZIkdRiQJUmSpA5vNS1JkjQOS5cuZWhoqNcali9fDjx0y+m+LFy4cKO+/bcBWZIkaYbYYost+i5hVjAgS5IkjcPGPGKqh3MOsiRJktRhQJYkSZI6DMiSJElShwFZkiRJ6jAgS5IkSR0GZEmSJKnDgCxJkiR1GJAlSZKkDgOyJEmS1GFAliRJkjoMyJIkSVKHAVmSJEnqMCBLkiRJHQZkSZIkqcOALEmSJHUYkCVJkqQOA7IkSZLUYUCWJEmSOgzIkiRJUocBWZIkSeowIEuSJEkdBmRJkiSpw4AsSZIkdQw0ICfZPMlFSS5PclWSD47SZr8kv05yWfvz/kHWJEmSJK3PnAH3fw+wf1WtTTIXuCDJd6rqwhHtzq+qQwZciyRJkrRBAx1Brsba9unc9qcGuU9JkibLzTffzDHHHMMtt9zSdymSptDA5yAn2TTJZcBNwLlV9eNRmu3TTsP4TpJnjdHPUUkuTnLxmjVrBlmyJEkAnHzyyVx++eWcdNJJfZciaQoNPCBX1QNVtSewANgrybNHNLkU2LWqfg/4OPDNMfo5saoWVdWi+fPnD7JkSZK4+eabOfPMM6kqzjrrLEeRpVlkyq5iUVW3A+cBB45YfsfwNIyqOguYm2TeVNUlSdJoTj75ZKqaWYEPPvigo8jSLDLoq1jMT7JN+3gL4OXANSPabJ8k7eO92pr8NV2S1KtzzjmH++67D4D77ruPc845p+eKJE2VQY8g7wB8P8kVwE9o5iB/O8nRSY5u2xwKXJnkcuB44PAa/pVdkqSeHHDAAcydOxeAuXPncsABB/RckaSpMtDLvFXVFcBzRll+QufxMmDZIOuQJGmijjjiCM4880wANtlkE4488sieK5I0VbyTniRJo5g3bx4HH3wwSTjooIPYbrvt+i5J0hQZ9I1CJEmasY444giuu+46R4+lWcaALEnSGObNm8cnPvGJvsuQNMWcYiFJkiR1GJAlSZKkDgOyJEmS1GFAliRJkjoMyJIkSVKHAVmSJEnqMCBLkiRJHQZkSZIkqcOALEmSJHV4J72eLF26lKGhoV5rWL58OQCLFy/utY6FCxeyZMmSXmuQJEkaZkCexbbYYou+S5AkSZp2DMg9ccRUkiRpenIOsiRJktRhQJYkSZI6DMiSJElShwFZkiRJ6jAgS5IkSR0GZEmSJKnDgCxJkiR1GJAlSZKkDgOyJEmS1GFAliRJkjoMyJIkSVKHAVmSJEnqSFX1XcOEJVkDrOi7jo3EPODmvouQOjwmNd14TGq68ZicPLtW1fyRC2dkQNbkSXJxVS3quw5pmMekphuPSU03HpOD5xQLSZIkqcOALEmSJHUYkHVi3wVII3hMarrxmNR04zE5YM5BliRJkjocQZYkSZI6DMiSJElShwF5BknyQJLLklyZ5Iwk2zzKfo5IsmyM5WvafVyV5NQkj59g39cnmfdo6tLGp3PMDv/stp62+yV54RSWpxlkxOff1zb02ZTkvCSL2seT9rmU5C/Ws+5tSX6W5Iq2zldvoK892tf00yRPm4z6NLMkeW2SSrJH+3zHJKeO0Xa/JN9+DPt6TNvPNgbkmWVdVe1ZVc8GbgWOGcA+vtLu41nAvcBhA9iHZo/hY3b45/r1tN0PMCBrLN3Pv3uBo3uqY9SAnGQB8D+BfavqPwMvAK7YQF+vAb5VVc+pqv83qVVqpngjcAFwOEBV3VBVh45slGTOVBc22xmQZ65/A3YCSPK0JGcnuSTJ+Z3fRF+V5Mft6MT3kjx5vJ23/xi3BG5rn89PclqSn7Q/L2qXb5fku+0+Pg1ksl+oNi7d0bwki9qRvt1oAs9/a0fUXpxk1yT/3I7G/XOSXdptTk5yfJJ/TXJtkkf8Z6KN3vnAwpEjYkmWJTlivJ0k2TzJSe2o70+TvLRd/rBv2ZJ8u93X3wFbtMfoF0Z09yTgTmAtQFWtrarr2u33THJheyx/I8kTkxwELAH+OMn323bfbD/Hr0py1KN4XzSDJNkKeBHwdtqAnGS3JFe2j49ovy05A/huu9kT2mPo6iQnJNmkbfuKJP+W5NJ2m63a5QcmuSbJBcDrpvo1zmQG5BkoyabAy4DT20UnAu+qqucB7wY+2S6/AHhBVT0H+DLw3nF0f1iSy4BfAtsCZ7TLPwZ8tKqeD/wh8Jl2+QeAC9p9nA7s8hhemjY+w2HisiTfGKtRO7J8As0xtmdVnQ8sAz7XjsZ9ATi+s8kOwL7AIcDfDax6TTvtL++vBH42Cd0dA1BVv0szkvfZJJuP1biq3sdDI9lvGrH6cuBXwHVt6H5VZ93ngD9rj+WfAR+oqrN46Jh/advube3n+CLg2CTbTcJr1PT1GuDsqvp34NYkzx2lzT7AW6tq//b5XsD/AH4XeBrwunbA4S+Bl1fVc4GLgf/eHsv/ALwKeDGw/SBfzMbGIfuZZYs2vO4GXAKc2/6W+ELga8lvB28f1/65APhKkh2AzYDrxrGPr1TV4jSdfQJ4D00AeTnwzM4+npBka+AltL+VVtWZSW57TK9QG5t1VbXno9x2Hx4a8fg88KHOum9W1YPA1RP5ZkQz2vDnHzQjyP/IY5+Ssy/wcYCquibJCuDpj6ajqnogyYHA82kGMD6a5HnAR4FtquoHbdPPAl8bo5tjk7y2fbwzsDtwy6OpRzPCG4Gl7eMvt88/MaLNuVV1a+f5RVV1LUCSL9Ecw3cDzwR+1P4fvRnNt8x7ANdV1fK2/SmA30yMkwF5ZllXVXsm+R3g2zSjHycDt48RQj4O/N+qOj3JfsBfj3dHVVXt1zrvognImwD7VNW6brv2H6MX09ZE3M9D316NOVo3iu5xdk/nsdN6ZodH/LKVpHsswQaOpyTHAO9onx7E2MfOhPodVs2NBS4CLkpyLnASTUDeoPYz+uU0n7O/SXLeePermaf9dmB/4NlJCtiU5jPukyOa3jXi+cj/b4vmOD63qt44Yh97jtJe4+QUixmoqn4NHEsznWIdzVd6rwdI4/fapr9DM1UC4K2PYlf7AsMnjnwXWDy8ov2HB/BD4E3tslcCT3wU+9Hscj3wvPbxH3aW3wls3Xn+r7Tz8miOsQsGXplmmhU032w9rh04eNn6GlfVJzonjN7Awz+/nk4zRewXNMfonkk2SbIzzdfaw+5LMndk32muPtD9inxPYEX7eX1bkhe3y/8I+MHI7Wk+r29rw/EeNCf5aeN1KM0Usl2rareq2pnmW94FG9huryRPaeceH0bzuXgh8KIkCwGSPL49nq8BnpKHrpDyxlF71KgMyDNUVf2UZs7b4TQf8G9PcjlwFTB8aaG/ppl6cT5w8zi7PqydL3oF8Bzgb9rlxwKL2pNMruahM8g/CLwkyaXAK4D/eGyvTLPAB4GPtcflA53lZwCvbY+/F9Mcc0e2x+IfAX869aVqOquqlcBXaa4W8QXgpxPs4pPApkl+BnwFOKKq7gF+RBNWfgZ8BLi0s82JwBWjnKQ3F/hIe0LUZTThZfiYfSvw4fZY3hM4bpRazgbmtG3+hib0aOP1RmDkeRmnMcZVUjr+jeZb3StpjtFvVNUa4AjgS+3xcyGwR1XdTTOl4sz2JL0Vk1f+xs9bTUuSJEkdjiBLkiRJHQZkSZIkqcOALEmSJHUYkCVJkqQOA7IkSZLUYUCWJEmSOgzIktSjJLsluXIQ2ybZM8lBj746SZqdDMiStPHak+aWypKkCTAgS1L/5iT5bHunylPbW8U+L8kPklyS5JwkOwC0yy9P8m/AMWN1mGQzmju2Dd8d87Aky5PMb9dvkmQoybwkJyc5Icn5Sf49ySFtm02TfDjJT9ra3jkF74Uk9c6ALEn9ewZwYlX9Z+AOmuD7ceDQqnoe8E/A37ZtTwKOrap91tdhVd0LvB/4SlXtWVVfAU6huTU9wMuBy6tq+Db0uwG/DxwMnJBkc+DtwK+r6vnA84F3JHnKZLxgSZrODMiS1L+VVfWj9vEpwAHAs4Fzk1wG/CWwIMnvANtU1Q/atp+f4H7+CXhL+/htNGF72Fer6sGqWg5cC+wBvAJ4S1vDj4HtgN0nuE9JmnHm9F2AJIka8fxO4KqRo8RJthml7fh3UrUyya+S7A/szUOjyaPVUECAd1XVOY92n5I0EzmCLEn92yXJcBh+I3AhMH94WZK5SZ5VVbcDv06yb9v2TY/s6mHuBLYesewzNKPUX62qBzrLX9/OS34a8FTgF8A5wJ8kmdvW8fQkWz66lyhJM4cBWZL693PgrUmuALalnX8M/H2Sy4HLgBe2bY8EPtGepLduA/1+H3jm8El67bLTga14+PQKaALxD4DvAEdX1d00Yfpq4NL2cnKfxm8eJc0CqXrU39ZJkmaYJIuAj1bVizvLTga+XVWn9laYJE0jjgRI0iyR5H3An7DhqRmSNKs5gixJM1ySA4C/H7H4uqp6bR/1SNJMZ0CWJEmSOjxJT5IkSeowIEuSJEkdBmRJkiSpw4AsSZIkdfx/LcqdBNyh+KYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bed_type\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='bed_type',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('bed_type')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs bed_type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_rating\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_rating',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_rating')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_accuracy\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_accuracy',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_accuracy')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_cleanliness\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_cleanliness',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_cleanliness')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_cleanliness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_checkin\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_checkin',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_checkin')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_checkin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_communication\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_communication',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_communication')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_communication')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_location\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_location',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_location')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_location')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#review_scores_value\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='review_scores_value',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('review_scores_value')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs review_scores_value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#instant_bookable\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='instant_bookable',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('instant_bookable')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs instant_bookable')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cancellation_policy\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='cancellation_policy',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('cancellation_policy')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs cancellation_policy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#require_guest_profile_picture\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='require_guest_profile_picture',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('require_guest_profile_picture')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs require_guest_profile_picture')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#require_guest_phone_verification\n",
    "plt.figure(figsize = (10,4))\n",
    "sns.boxplot(x='require_guest_phone_verification',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('require_guest_phone_verification')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs require_guest_phone_verification')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#require_guest_phone_verification\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x='location',y='log_price',data=train,palette='Blues')\n",
    "plt.tight_layout()\n",
    "plt.xlabel('location')\n",
    "plt.ylabel('log_price')\n",
    "plt.title('Trainning: log_price vs location')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3. Dummy variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_time_within 12 hours</th>\n",
       "      <th>host_response_time_within a day</th>\n",
       "      <th>host_response_time_within a few hours</th>\n",
       "      <th>host_response_time_within an hour</th>\n",
       "      <th>host_is_superhost_t</th>\n",
       "      <th>host_identity_verified_t</th>\n",
       "      <th>property_type_House</th>\n",
       "      <th>property_type_Other</th>\n",
       "      <th>property_type_Townhouse</th>\n",
       "      <th>room_type_Hotel room</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value_No Review</th>\n",
       "      <th>instant_bookable_t</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
       "      <th>cancellation_policy_super_strict_30</th>\n",
       "      <th>cancellation_policy_super_strict_60</th>\n",
       "      <th>require_guest_profile_picture_t</th>\n",
       "      <th>require_guest_phone_verification_t</th>\n",
       "      <th>location_average</th>\n",
       "      <th>location_below_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_response_time_within 12 hours  host_response_time_within a day  \\\n",
       "0                                   0                                0   \n",
       "1                                   1                                0   \n",
       "2                                   0                                0   \n",
       "3                                   0                                0   \n",
       "4                                   0                                1   \n",
       "\n",
       "   host_response_time_within a few hours  host_response_time_within an hour  \\\n",
       "0                                      0                                  1   \n",
       "1                                      0                                  0   \n",
       "2                                      0                                  1   \n",
       "3                                      0                                  1   \n",
       "4                                      0                                  0   \n",
       "\n",
       "   host_is_superhost_t  host_identity_verified_t  property_type_House  \\\n",
       "0                    1                         0                    0   \n",
       "1                    1                         0                    0   \n",
       "2                    0                         0                    0   \n",
       "3                    0                         0                    0   \n",
       "4                    0                         0                    0   \n",
       "\n",
       "   property_type_Other  property_type_Townhouse  room_type_Hotel room  ...  \\\n",
       "0                    1                        0                     1  ...   \n",
       "1                    1                        0                     0  ...   \n",
       "2                    0                        0                     0  ...   \n",
       "3                    0                        0                     0  ...   \n",
       "4                    0                        0                     0  ...   \n",
       "\n",
       "   review_scores_value_No Review  instant_bookable_t  \\\n",
       "0                              0                   1   \n",
       "1                              0                   1   \n",
       "2                              0                   0   \n",
       "3                              0                   1   \n",
       "4                              1                   0   \n",
       "\n",
       "   cancellation_policy_moderate  \\\n",
       "0                             0   \n",
       "1                             1   \n",
       "2                             0   \n",
       "3                             0   \n",
       "4                             1   \n",
       "\n",
       "   cancellation_policy_strict_14_with_grace_period  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                1   \n",
       "3                                                1   \n",
       "4                                                0   \n",
       "\n",
       "   cancellation_policy_super_strict_30  cancellation_policy_super_strict_60  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   require_guest_profile_picture_t  require_guest_phone_verification_t  \\\n",
       "0                                0                                   0   \n",
       "1                                0                                   0   \n",
       "2                                0                                   0   \n",
       "3                                0                                   0   \n",
       "4                                0                                   0   \n",
       "\n",
       "   location_average  location_below_average  \n",
       "0                 1                       0  \n",
       "1                 0                       0  \n",
       "2                 0                       1  \n",
       "3                 1                       0  \n",
       "4                 1                       0  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummy variables in train data\n",
    "dummy_train= pd.get_dummies(train[categorical_data], drop_first= True)\n",
    "dummy_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value_No Review</th>\n",
       "      <th>instant_bookable_t</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
       "      <th>cancellation_policy_super_strict_30</th>\n",
       "      <th>cancellation_policy_super_strict_60</th>\n",
       "      <th>require_guest_profile_picture_t</th>\n",
       "      <th>require_guest_phone_verification_t</th>\n",
       "      <th>location_average</th>\n",
       "      <th>location_below_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.293305</td>\n",
       "      <td>199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>-33.918732</td>\n",
       "      <td>151.242035</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.553877</td>\n",
       "      <td>95</td>\n",
       "      <td>0.948178</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1</td>\n",
       "      <td>-33.698425</td>\n",
       "      <td>151.290979</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.049856</td>\n",
       "      <td>156</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>8</td>\n",
       "      <td>-33.847388</td>\n",
       "      <td>151.072890</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>100</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>260</td>\n",
       "      <td>-33.870261</td>\n",
       "      <td>151.195131</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828026</td>\n",
       "      <td>1</td>\n",
       "      <td>-33.908168</td>\n",
       "      <td>151.211849</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price  price  host_response_rate  host_acceptance_rate  \\\n",
       "0   5.293305    199            1.000000              1.000000   \n",
       "1   4.553877     95            0.948178              0.830000   \n",
       "2   5.049856    156            0.910000              0.980000   \n",
       "3   4.605170    100            0.990000              0.970000   \n",
       "4   4.605170    100            1.000000              0.828026   \n",
       "\n",
       "   host_listings_count   latitude   longitude  accommodates  bathrooms  \\\n",
       "0                    5 -33.918732  151.242035             2        1.0   \n",
       "1                    1 -33.698425  151.290979             4        1.0   \n",
       "2                    8 -33.847388  151.072890             4        1.0   \n",
       "3                  260 -33.870261  151.195131             2        1.0   \n",
       "4                    1 -33.908168  151.211849             1        1.0   \n",
       "\n",
       "   bedrooms  ...  review_scores_value_No Review  instant_bookable_t  \\\n",
       "0       0.0  ...                              0                   1   \n",
       "1       1.0  ...                              0                   1   \n",
       "2       2.0  ...                              0                   0   \n",
       "3       1.0  ...                              0                   1   \n",
       "4       1.0  ...                              1                   0   \n",
       "\n",
       "   cancellation_policy_moderate  \\\n",
       "0                             0   \n",
       "1                             1   \n",
       "2                             0   \n",
       "3                             0   \n",
       "4                             1   \n",
       "\n",
       "   cancellation_policy_strict_14_with_grace_period  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                1   \n",
       "3                                                1   \n",
       "4                                                0   \n",
       "\n",
       "   cancellation_policy_super_strict_30  cancellation_policy_super_strict_60  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   require_guest_profile_picture_t  require_guest_phone_verification_t  \\\n",
       "0                                0                                   0   \n",
       "1                                0                                   0   \n",
       "2                                0                                   0   \n",
       "3                                0                                   0   \n",
       "4                                0                                   0   \n",
       "\n",
       "   location_average  location_below_average  \n",
       "0                 1                       0  \n",
       "1                 0                       0  \n",
       "2                 0                       1  \n",
       "3                 1                       0  \n",
       "4                 1                       0  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine dummy variables with numerical variables in train data\n",
    "final_train = pd.concat([new_numerical_data,dummy_train],axis=1)\n",
    "final_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value_No Review</th>\n",
       "      <th>instant_bookable_t</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
       "      <th>cancellation_policy_super_strict_30</th>\n",
       "      <th>cancellation_policy_super_strict_60</th>\n",
       "      <th>require_guest_profile_picture_t</th>\n",
       "      <th>require_guest_phone_verification_t</th>\n",
       "      <th>location_average</th>\n",
       "      <th>location_below_average</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.938955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>-33.874641</td>\n",
       "      <td>151.225248</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.938955</td>\n",
       "      <td>0.819867</td>\n",
       "      <td>2</td>\n",
       "      <td>-33.898198</td>\n",
       "      <td>151.178277</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1</td>\n",
       "      <td>-34.044786</td>\n",
       "      <td>151.145606</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_response_rate  host_acceptance_rate  host_listings_count   latitude  \\\n",
       "0            1.000000              0.570000                    1 -33.776680   \n",
       "1            0.000000              1.000000                    1 -33.795694   \n",
       "2            0.938955              1.000000                    1 -33.874641   \n",
       "3            0.938955              0.819867                    2 -33.898198   \n",
       "4            1.000000              0.850000                    1 -34.044786   \n",
       "\n",
       "    longitude  accommodates  bathrooms  bedrooms  beds  security_deposit  ...  \\\n",
       "0  151.261740             1        1.0       1.0   1.0               0.0  ...   \n",
       "1  151.155964             4        1.0       2.0   2.0             500.0  ...   \n",
       "2  151.225248             2        1.0       0.0   1.0             800.0  ...   \n",
       "3  151.178277             2        1.0       1.0   1.0               0.0  ...   \n",
       "4  151.145606             2        1.0       1.0   1.0               0.0  ...   \n",
       "\n",
       "   review_scores_value_No Review  instant_bookable_t  \\\n",
       "0                              0                   0   \n",
       "1                              0                   0   \n",
       "2                              0                   0   \n",
       "3                              0                   0   \n",
       "4                              0                   0   \n",
       "\n",
       "   cancellation_policy_moderate  \\\n",
       "0                             0   \n",
       "1                             0   \n",
       "2                             0   \n",
       "3                             1   \n",
       "4                             0   \n",
       "\n",
       "   cancellation_policy_strict_14_with_grace_period  \\\n",
       "0                                                0   \n",
       "1                                                1   \n",
       "2                                                1   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   cancellation_policy_super_strict_30  cancellation_policy_super_strict_60  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   require_guest_profile_picture_t  require_guest_phone_verification_t  \\\n",
       "0                                0                                   0   \n",
       "1                                0                                   0   \n",
       "2                                0                                   0   \n",
       "3                                0                                   0   \n",
       "4                                0                                   0   \n",
       "\n",
       "   location_average  location_below_average  \n",
       "0                 0                       0  \n",
       "1                 0                       1  \n",
       "2                 1                       0  \n",
       "3                 1                       0  \n",
       "4                 1                       0  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine dummy variables with numerical variables in test data\n",
    "dummy_test= pd.get_dummies(test[categorical_data], drop_first= True)\n",
    "final_test = pd.concat([new_test_numerical_data,dummy_test],axis=1)\n",
    "final_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Modelling\n",
    "---------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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